The stage of a disease is a measure of how far the disease has progressed in its natural history, with the end stage usually resulting in the death of a patient with the disease and/or the failure of an organ with the disease. In other words, the grade (or stage) of a disease reflects how quickly the disease is progressing to the end stage [1 ].
In most forms of chronic liver diseases, the end stage involves a large amount of fibrosis or cirrhosis, whereas lower amounts of fibrosis or cirrhosis are present in the liver during the earlier stages. Descriptive or semi-quantitative scoring systems may be used to grade and stage liver biopsy samples. For example, a traditional method of assessing the degree of fibrosis in a liver biopsy sample may involve giving the liver biopsy sample a grade (“absent”, “mild”, “moderate”, or “severe”) based on the amount of fibrosis in the liver biopsy sample. Furthermore, in a first semi-quantitative scoring system implemented in the 1980s, a range of numbers representing different categories was allocated to different pathological features on the basis of their severity [2]. Examples of routinely used scoring systems also include the Knodell histological activity index (HAI) [3], Scheuer [4], Ishak [5] and METAVIR[6] systems. However, pathological features used in these systems are usually not clearly defined and are somewhat ambiguous. As a result, the grading and staging scores obtained in these systems tend to rely on the observers'subjective opinions. Therefore, using these systems, inter- and intra-observer variations can be as high as 35%, making it difficult to obtain highly reproducible results [7-9].
In several studies as shown in Table 1 [10-17], liver fibrosis is quantified using image analysis. The computer-aided systems in these studies aim to provide objective quantitative measurements which can help reduce observer discrepancies. However, all of the systems in the studies shown in Table 1 require stained biopsy samples and thus, are faced with the problem of staining variations. Also, in most of these systems, the only measurement to grade and stage fibrosis is the fibrosis area. However, other pathological features (such as fibrosis architecture) often play an equally important, if not more important, role in grading and staging fibrosis [2]. Furthermore, although some correlations between the fibrosis area and the semi-quantitative scores were reported in the studies, the amount of fibrosis is not specifically addressed in any of the scoring systems.
TABLE 1SampleQuantificationPreparationParameter(s)Algorithm(s)AuthorsYearTechniqueMeasuredUsedM. O'Brien et2000MalloryFibrosis areaManualal. [11]trichromethresholdingstainedM. Masseroli2000Sirius RedPerisinusoidal,Kurita'set al. [10]stainedPortal-peri-thresholdingportal, Septalfibrosis areaWright M et2003PicrosiriusFibrosis areaThresholdingal. [12]Red stainedusinghistogramsA. Lazzarini2005TrichromeFibrosis areaManualet al. [13]stainedthresholdingM Friedenberg2005Masson'sFibrosis areaManualet al. [14]trichromethresholdingstainedI Matalka et2006Van Geison-54 imageTwo-levelal. [15]stainedfeaturesNeural NetworkZ. D Goodman2007Sirius RedFibrosis area,Opticalet al. [16]stainedFibrosispolarizationvolumeimagingN. Dioguardi2008Sirius RedSurvivalFractalet al. [17]stainedparenchymageometry,surface,Hurst'sInflammatoryexponent,basin,ClusteringCoefficient ofinflammatoryactivity,Fractaldimension ofthe fibrosis,Wrinkedness offibrosis,Tectonic index